Key-value stores provide users simple yet powerful interface to data storage, which are often used in complicated systems.  LMDB is a framework that provides high-performance key-value storage
"The ever-increasing appetite of businesses to embrace emerging big data-related software and infrastructure technologies while keeping the implementation costs low has led to the creation of a rich ecosystem of new and incumbent suppliers," said Ashish Nadkarni, IDC program director, enterprise servers and storage, in a prepared statement. Nadkarni co-authored the IDC report with Dan Vesset, program VP, business analytics & big data. "At the same time, the
Volume – Businesses and organizations collect data from a large selection of sources. These include business transactions, social media and information from sensor or machine-to-machine data. Storing these data collections would have been a problem in the past, but with emerging technologies like cloud computing, it has become easily possible.
In 1999,Steve Bryson, David Kenwright, Michael Cox, David Ellsworth, Robert Haimes published Visually exploring gigabyte data sets in real time, which is the first CACM article that uses the term “Big Data” . Big Data defines the data sets with gazillion information that cannot be crawled, managed, or processed by traditional tools in a certain amount of time. It also represents the techniques to extract valuable information quickly from various information of large-amount data. Some common technologies that are applicable to big data are massively parallel processor (MPP) database, data mining, distributed file system, distributed database, cloud computing, network, and extensible memory system.
Key value stores: It has a hash table which consists of a primary key and a pointer to the particular data in the database. Amazon Dynamo is one of the most common key values for Nosql database.
Lately with the development of distributed computing, issues services that utilizes web and require enormous amount of data come to forefront. For Organizations like Facebook and Google the web has developed has a vast, distributed data repository for which handling by conventional DBMS is not sufficient. Rather than extending on the hardware capabilities, a more realistic approach has been accepted. Technically, it is an instance of scaling through dynamic adding servers from the reasons increasing either information volume in the repository or the number of users of this repository. In this scenario, the big data problem is frequently examined and in addition explained on a technological level of web databases.
STRUCTURE OF DATA: The data structure of a relational database comprises of table structure. Every table is identified by a unique name or label. The data tables are described as the collection of rows and columns. Each row of the table is known as the record and each column is known as the field of the specific data table. All the data sets are well organized and logical linked to each other through definite and unique relationships. A table, therefore can also be defined as the “structured collection of relationships”. The fundamental aim of developing No SQL database systems is to easily and effectively handle vast quantities of data or information in advanced web-scale applications. In order to achieve this purpose, the No SQL systems are designed as the schema-free database systems. There are different modes to define the No SQL databases that typically depend on the requirements of the data that has to be managed. The data model for key-value store No SQL database is
NoSQL databases had made for unraveling the Big Data issue by utilizing a distributed system to bring out excellent performance in data storage and retrieval at very large-scale. At this scale, pieces of the system often fail and NoSQL is created to handle these failures (Chow, 2013) (Ron, Shulman-Peleg, & Bronshtein, 2015). Various companies have espouse different sorts of non-relational databases, ordinarily alluded to as
Data has always been analyzed within companies and used to help benefit the future of businesses. However, the evolution of how the data stored, combined, analyzed and used to predict the pattern and tendencies of consumers has evolved as technology has seen numerous advancements throughout the past century. In the 1900s databases began as “computer hard disks” and in 1965, after many other discoveries including voice recognition, “the US Government plans the world’s first data center to store 742 million tax returns and 175 million sets of fingerprints on magnetic tape.” The evolution of data and how it evolved into forming large databases continues in 1991 when the internet began to pop up and “digital storage became more cost effective than paper. And with the constant increase of the data supplied digitally, Hadoop was created in 2005 and from that point forward there was “14.7 Exabytes of new information are produced this year" and this number is rapidly increasing with a lot of mobile devices the people in our society have today (Marr). The evolution of the internet and then the expansion of the number of mobile devices society has access to today led data to evolve and companies now need large central Database management systems in order to run an efficient and a successful business.
As there is a rise in data volumes, the manageability of data and storing these huge volumes of data became a cause of concern to most of the organizations. It was during this period when Number of SQL or more popularly NoSQL was introduced, to process these large amounts of data efficiently and effectively. For this purpose, various Data Store categories were developed, based on the different data models. Some of the categories are:
In Nowadays, there are two major of database management systems which are used to deal with data, the first one called Relational Database Management System (RDBMS) which is the traditional relational databases, it deals with structured data and have been popular since decades since 1970, while the second one called Not only Structure Query Language databases (NoSQL), they are dealing with semi-structured and unstructured data; the NoSQL types are gaining their popularity with the development of the internet and the social media since April 2009. NoSQL are intending to override the cons of RDBMs, such as fixed
Data is very critical for any organization. In an organization every by year massive amounts of data will be created and how fast your business reacts to that important information determines whether you succeed or fail. The big problem is how we efficiently handle the 3 V’s of Big Data.
Currently, there are two major of database management systems which are used to deal with data, the first one called Relational Database Management System (RDBMS) which is the traditional relational databases, it deals with structured data and have been popular since decades from 1970, while the second one called Not only Structure Query Language databases (NoSQL), they have been dealing with semi-structured and unstructured data; the NoSQL term was introduced for the first time in 1998 by Carlo Strozzi and Eric Evans reintroduced the term NoSQL in early 2009, and now the NoSQL types are gaining their popularity with the development of the internet and the social media. NoSQL are intending to override the cons of RDBMS, such as fixed schemas, JOIN operations and handling the scalability problems. With the appearance of Big Data,